Muhammad Junaid Arshad

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain.

The transition from traditional energy or electrical grids to smart energy or electrical grids has significantly transformed energy management. This evolution emphasizes decentralization, efficiency, and sustainability in energy systems. However, it also introduces numerous risks, including cyber-physical system vulnerabilities and challenges in energy trading. The application of blockchain and Machine Learning (ML) offers potential solutions to these issues. Blockchain enhances energy transactions by making them safer, more transparent, and tamper-proof, while ML optimizes grid performance by improving predictions, fault detection, and anomaly identification. This systematic review examines the application of blockchain and ML in peer-to-peer (P2P) energy trading within smart grids and analyzes how these technologies complement each other in mitigating risks and enhancing the efficiency of smart grids. Blockchain enhances security by providing privacy for transactions and maintaining immutable records, while ML predicts market trends, identifies fraudulent activities, and ensures efficient energy use. The paper identifies critical challenges in smart grids, such as unsecured communication channels and vulnerabilities to cyber threats, and discusses how blockchain and ML address these issues. Furthermore, the study explores emerging trends, such as lightweight blockchain systems and edge computing, to overcome implementation challenges. A new architecture is proposed, integrating blockchain with ML algorithms to create resilient, secure, and efficient energy trading markets. The paper underscores the need for global standardization, improved cybersecurity measures, and further research into how blockchain and ML can revolutionize smart grids. This study integrates current knowledge with a forward-looking perspective, providing valuable insights for researchers, policymakers, and stakeholders in the energy sector to collaboratively build a future of efficient and intelligent energy systems.